Show dataset YOLO output readiness

README.md

@@ -54,6 +54,8 @@ checks YOLO txt labels and mask dimensions, and can generate a `dataset.yaml`
 for the `yolo.train_custom` task. The selected upload dataset also exposes
 direct YOLO custom train, predict, and heatmap actions; custom outputs are
 written under `var/custom_yolo_runs` and are scanned by the results dashboard.
+When a dataset is selected, the dataset panel shows its custom YOLO `best.pt`,
+prediction previews, heatmap previews, and detected training curves.
 Segmentation previews, YOLO heatmaps, and loss/metric artifacts are grouped on
 the results dashboard, and YOLO-style `results.csv` files are parsed into
 lightweight training curves.

backend/app/agents/evaluation_agent.py

@@ -44,6 +44,7 @@ def evaluate_project() -> dict:
         "upload_ui": "uploadDatasetFiles" in frontend_text and "labels" in frontend_text and "masks" in frontend_text,
         "dataset_quality_ui": "DatasetQuality" in frontend_text and "generateSelectedYoloYaml" in frontend_text,
         "uploaded_yolo_workflow_ui": "startSelectedYoloTrain" in frontend_text and "startSelectedYoloPredict" in frontend_text and "startSelectedYoloHeatmap" in frontend_text,
+        "dataset_yolo_outputs_ui": "DatasetYoloOutputs" in frontend_text and "selectedYoloOutputs" in frontend_text and "BEST.PT READY" in frontend_text,
         "loss_result_ui": "loss" in frontend_text.lower() and "heatmap" in frontend_text.lower() and "CurvePanel" in frontend_text,
         "job_progress_ui": "JobProgressBar" in frontend_text and "progressTrack" in frontend_text,
         "runtime_readiness_ui": "runtimeReadiness" in frontend_text and "环境就绪" in frontend_text,

backend/app/modules/system/service.py

@@ -14,6 +14,7 @@ import fcntl
 from ...config import settings
 
 READINESS_CACHE_SECONDS = 300
+READINESS_FAILURE_CACHE_SECONDS = 30
 _readiness_cache: tuple[float, dict[str, Any]] | None = None
 _readiness_thread_lock = threading.Lock()
 
@@ -215,18 +216,22 @@ def inspect_conda_env(env_name: str, required_imports: list[dict[str, str]], tim
 def get_runtime_readiness(force: bool = False) -> dict[str, Any]:
     global _readiness_cache
     now = time.time()
-    if not force and _readiness_cache and now - _readiness_cache[0] < READINESS_CACHE_SECONDS:
+    if not force and _readiness_cache:
-        cached = dict(_readiness_cache[1])
+        cache_ttl = READINESS_CACHE_SECONDS if _readiness_cache[1].get("passed") else READINESS_FAILURE_CACHE_SECONDS
-        cached["cached"] = True
+        if now - _readiness_cache[0] < cache_ttl:
-        return cached
-
-    with readiness_probe_lock():
-        now = time.time()
-        if not force and _readiness_cache and now - _readiness_cache[0] < READINESS_CACHE_SECONDS:
             cached = dict(_readiness_cache[1])
             cached["cached"] = True
             return cached
 
+    with readiness_probe_lock():
+        now = time.time()
+        if not force and _readiness_cache:
+            cache_ttl = READINESS_CACHE_SECONDS if _readiness_cache[1].get("passed") else READINESS_FAILURE_CACHE_SECONDS
+            if now - _readiness_cache[0] < cache_ttl:
+                cached = dict(_readiness_cache[1])
+                cached["cached"] = True
+                return cached
+
         conda = get_conda_envs()
         env_paths = {item["name"]: item["path"] for item in conda.get("envs", [])}
         envs: list[dict[str, Any]] = []
@@ -254,6 +259,7 @@ def get_runtime_readiness(force: bool = False) -> dict[str, Any]:
             "passed": bool(conda.get("available")) and all(item["passed"] for item in envs),
             "generated_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime(now)),
             "cache_seconds": READINESS_CACHE_SECONDS,
+            "failure_cache_seconds": READINESS_FAILURE_CACHE_SECONDS,
             "cached": False,
             "envs": envs,
             "specs": {

backend/tests/test_system_service.py

@@ -71,3 +71,15 @@ def test_runtime_readiness_aggregates_probe_results(monkeypatch):
     assert readiness["passed"] is True
     assert readiness["envs"][0]["path"] == "/envs/seg_smp"
     assert readiness["specs"]["env_files"] == ["envs/seg_smp.yml", "envs/seg_mmcv.yml"]
+
+
+def test_runtime_readiness_refreshes_stale_failure_cache(monkeypatch):
+    monkeypatch.setattr(service, "_readiness_cache", (100.0, {"passed": False, "envs": [], "available": True}))
+    monkeypatch.setattr(service.time, "time", lambda: 100.0 + service.READINESS_FAILURE_CACHE_SECONDS + 1)
+    monkeypatch.setattr(service, "runtime_environment_specs", lambda: [])
+    monkeypatch.setattr(service, "get_conda_envs", lambda: {"available": True, "envs": []})
+
+    readiness = get_runtime_readiness(force=False)
+
+    assert readiness["cached"] is False
+    assert readiness["passed"] is True

frontend/src/main.tsx

@@ -115,6 +115,14 @@ type TrainingCurve = {
   series: CurveSeries[];
 };
 
+type DatasetYoloOutputsPayload = {
+  bestWeight?: ResultItem;
+  artifacts: ResultItem[];
+  curves: TrainingCurve[];
+  predictions: ResultItem[];
+  heatmaps: ResultItem[];
+};
+
 type CoveragePayload = {
   scripts_total: number;
   user_scripts_total: number;
@@ -352,7 +360,7 @@ function useData() {
       setRuntimeReadiness(readinessNext);
       setCapabilities(capabilitiesNext);
       setJobs(jobsNext);
-      setResults(resultsNext.slice(0, 80));
+      setResults(resultsNext.slice(0, 240));
       setCurves(curvesNext.slice(0, 12));
       setDatasets(datasetsNext);
       const validationEntries: Array<[string, DatasetValidation]> = [];
@@ -444,6 +452,26 @@ function App() {
   );
   const selectedValidation = selectedDataset ? datasetValidations[selectedDataset.name] : undefined;
   const selectedCurve = curves.find((curve) => curve.relative_path === selectedCurvePath) ?? curves[0];
+  const selectedYoloOutputs = useMemo<DatasetYoloOutputsPayload>(() => {
+    if (!selectedDataset) {
+      return { artifacts: [], curves: [], predictions: [], heatmaps: [] };
+    }
+    const prefixes = [
+      `var/custom_yolo_runs/${selectedDataset.name}`,
+      `var/custom_yolo_runs/${selectedDataset.name}_predict`,
+      `var/custom_yolo_runs/${selectedDataset.name}_heatmap`
+    ];
+    const matches = (relativePath: string) => prefixes.some((prefix) => relativePath === prefix || relativePath.startsWith(`${prefix}/`));
+    const artifacts = results.filter((item) => matches(item.relative_path));
+    return {
+      bestWeight: artifacts.find((item) => item.relative_path.endsWith("/weights/best.pt")),
+      artifacts,
+      curves: curves.filter((curve) => matches(curve.relative_path)),
+      predictions: artifacts.filter((item) => item.role === "segmentation" && item.previewable),
+      heatmaps: artifacts.filter((item) => item.role === "heatmap" && item.previewable)
+    };
+  }, [curves, results, selectedDataset]);
+  const selectedYoloWeightReady = Boolean(selectedYoloOutputs.bestWeight);
 
   function pickTask(next: string) {
     setTaskType(next);
@@ -562,7 +590,7 @@ function App() {
 
   function customYoloWeightPath(dataset: UploadedDataset) {
     const expected = `var/custom_yolo_runs/${dataset.name}/weights/best.pt`;
-    return results.find((item) => item.relative_path === expected || item.relative_path.endsWith(`/custom_yolo_runs/${dataset.name}/weights/best.pt`))?.relative_path ?? expected;
+    return selectedYoloOutputs.bestWeight?.relative_path ?? results.find((item) => item.relative_path === expected || item.relative_path.endsWith(`/custom_yolo_runs/${dataset.name}/weights/best.pt`))?.relative_path ?? expected;
   }
 
   async function createSelectedYoloYaml() {
@@ -879,10 +907,10 @@ function App() {
                 <button className="iconButton" disabled={busy || !selectedValidation?.ready.yolo} onClick={startSelectedYoloTrain} title="启动自定义 YOLO 训练">
                   <Play size={18} />
                 </button>
-                <button className="iconButton" disabled={busy || !selectedDataset?.absolute_layout} onClick={startSelectedYoloPredict} title="使用自定义 best.pt 预测">
+                <button className="iconButton" disabled={busy || !selectedDataset?.absolute_layout || !selectedYoloWeightReady} onClick={startSelectedYoloPredict} title={selectedYoloWeightReady ? "使用自定义 best.pt 预测" : "best.pt 尚未生成"}>
                   <FileImage size={18} />
                 </button>
-                <button className="iconButton" disabled={busy || !selectedDataset?.absolute_layout} onClick={startSelectedYoloHeatmap} title="使用自定义 best.pt 生成热度图">
+                <button className="iconButton" disabled={busy || !selectedDataset?.absolute_layout || !selectedYoloWeightReady} onClick={startSelectedYoloHeatmap} title={selectedYoloWeightReady ? "使用自定义 best.pt 生成热度图" : "best.pt 尚未生成"}>
                   <Zap size={18} />
                 </button>
                 <FileImage size={22} />
@@ -929,6 +957,7 @@ function App() {
               ))}
             </div>
             {selectedValidation && <DatasetQuality validation={selectedValidation} />}
+            {selectedDataset && <DatasetYoloOutputs dataset={selectedDataset} outputs={selectedYoloOutputs} />}
           </div>
         </section>
 
@@ -1258,6 +1287,48 @@ function DatasetQuality({ validation }: { validation: DatasetValidation }) {
   );
 }
 
+function DatasetYoloOutputs({ dataset, outputs }: { dataset: UploadedDataset; outputs: DatasetYoloOutputsPayload }) {
+  const previewItems = [...outputs.heatmaps.slice(0, 3), ...outputs.predictions.slice(0, 3)].slice(0, 6);
+  return (
+    <div className="datasetOutputBox">
+      <div className="qualityHead">
+        <strong>{dataset.name} · YOLO</strong>
+        <span>{outputs.bestWeight ? "BEST.PT READY" : "BEST.PT MISSING"}</span>
+      </div>
+      <div className="qualityStats">
+        <div><span>Weights</span><strong>{outputs.bestWeight ? 1 : 0}</strong></div>
+        <div><span>Predict</span><strong>{outputs.predictions.length}</strong></div>
+        <div><span>Heatmap</span><strong>{outputs.heatmaps.length}</strong></div>
+        <div><span>Curves</span><strong>{outputs.curves.length}</strong></div>
+      </div>
+      <div className="datasetOutputLinks">
+        {outputs.bestWeight && (
+          <a href={`${API_BASE}/api/artifacts/${outputs.bestWeight.relative_path}`} target="_blank" rel="noreferrer">
+            <span>best.pt</span>
+            <small>{formatBytes(outputs.bestWeight.size)}</small>
+          </a>
+        )}
+        {outputs.curves.slice(0, 2).map((curve) => (
+          <a key={curve.relative_path} href={`${API_BASE}/api/artifacts/${curve.relative_path}`} target="_blank" rel="noreferrer">
+            <span>{curve.name}</span>
+            <small>{curve.row_count} epochs</small>
+          </a>
+        ))}
+      </div>
+      {!!previewItems.length && (
+        <div className="datasetOutputPreview">
+          {previewItems.map((item) => (
+            <a key={item.relative_path} href={`${API_BASE}/api/artifacts/${item.relative_path}`} target="_blank" rel="noreferrer">
+              <img src={`${API_BASE}/api/artifacts/${item.relative_path}`} alt={item.name} />
+              <span>{item.role}</span>
+            </a>
+          ))}
+        </div>
+      )}
+    </div>
+  );
+}
+
 function CurvePanel({
   curves,
   selected,

frontend/src/styles.css

@@ -761,6 +761,16 @@ textarea {
   background: #0b0d0b;
 }
 
+.datasetOutputBox {
+  display: grid;
+  gap: 10px;
+  margin-top: 12px;
+  padding: 12px;
+  border: 1px solid var(--line);
+  border-radius: 7px;
+  background: #0b0d0b;
+}
+
 .qualityHead {
   display: flex;
   justify-content: space-between;
@@ -817,6 +827,65 @@ textarea {
   border-color: rgba(240, 113, 103, 0.55);
 }
 
+.datasetOutputLinks {
+  display: grid;
+  grid-template-columns: repeat(2, minmax(0, 1fr));
+  gap: 8px;
+}
+
+.datasetOutputLinks a {
+  min-width: 0;
+  display: grid;
+  grid-template-columns: minmax(0, 1fr) auto;
+  gap: 8px;
+  padding: 8px;
+  border-radius: 6px;
+  border: 1px solid var(--line);
+  background: #101310;
+  color: var(--ink);
+  text-decoration: none;
+}
+
+.datasetOutputLinks span,
+.datasetOutputLinks small {
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
+.datasetOutputPreview {
+  display: grid;
+  grid-template-columns: repeat(3, minmax(0, 1fr));
+  gap: 8px;
+}
+
+.datasetOutputPreview a {
+  min-width: 0;
+  overflow: hidden;
+  border-radius: 6px;
+  border: 1px solid var(--line);
+  background: #101310;
+  color: var(--muted);
+  text-decoration: none;
+}
+
+.datasetOutputPreview img {
+  display: block;
+  width: 100%;
+  aspect-ratio: 1.35 / 1;
+  object-fit: cover;
+  background: #060806;
+}
+
+.datasetOutputPreview span {
+  display: block;
+  padding: 6px;
+  font-size: 11px;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
 .jobList, .resultList {
   display: grid;
   gap: 8px;
@@ -1201,6 +1270,8 @@ meter {
 
   .opGrid,
   .sampleStrip,
+  .datasetOutputLinks,
+  .datasetOutputPreview,
   .taskCheckList,
   .agentCheckList,
   .qualityStats,